Classical Adiabatic Annealing in Memristor Hopfield Neural Networks for Combinatorial Optimization

Abstract

There is an intense search for supplements to digital computer processors to solve computationally hard problems, such as gene sequencing. Quantum computing has gained popularity in this search, which exploits quantum tunneling to achieve adiabatic annealing. However, quantum annealing requires very low temperatures and precise control, which lead to unreasonably high costs. Here we show via simulations, alongside experimental instantiations, that computational advantages qualitatively similar to those gained by quantum annealing can be achieved at room temperature in classical systems by using a memristor Hopfield neural network to solve computationally hard problems.